La pêche wolastoqey à l’oursin vert : Agencement socioécologique dans l’estuaire du Saint-Laurent
Bibliographic record
Abstract
Cette recherche explore les motivations inhérentes à la pêche commerciale de l’oursin vert par la Première Nation Wolastoqiyik Wahsipekuk. Cette pratique de pêche met en opposition une série de logiques : locales et globales, revendications territoriales et marchés étrangers, approches industrielles et préoccupations environnementales. L’article suggère que l’anthropologie contemporaine, à travers le concept d’agencement, permet une compréhension plus complète des réalités complexes de cette pratique. À partir de données ethnographiques recueillies dans l’industrie de l’oursin au Bas-Saint-Laurent, l’article soutient que cette pêche permet aux pêcheurs de négocier la préservation de leur mode d’occupation du territoire ancestral malgré les pressions écologiques, économiques et politiques propres à l’époque contemporaine. L’article suggère également que la prise en compte de cette négociation ouvre des perspectives vers lesquelles repenser nos modes d’approvisionnements alimentaires inadaptés aux réalités fluctuantes du monde contemporain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".